Improve model card for Neon: Negative Extrapolation From Self-Training Improves Image Generation

README.md

@@ -1,3 +1,152 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: unconditional-image-generation
+---
+
+# Neon: Negative Extrapolation From Self-Training Improves Image Generation
+
+Paper: [Neon: Negative Extrapolation From Self-Training Improves Image Generation](https://huggingface.co/papers/2510.03597)
+
+Code: [https://github.com/SinaAlemohammad/Neon](https://github.com/SinaAlemohammad/Neon)
+
+## Introduction
+
+Scaling generative AI models is bottlenecked by the scarcity of high-quality training data. The ease of synthesizing from a generative model suggests using (unverified) synthetic data to augment a limited corpus of real data for the purpose of fine-tuning in the hope of improving performance. Unfortunately, however, the resulting positive feedback loop leads to model autophagy disorder (MAD, aka model collapse) that results in a rapid degradation in sample quality and/or diversity. In this paper, we introduce Neon (for Negative Extrapolation frOm self-traiNing), a new learning method that turns the degradation from self-training into a powerful signal for self-improvement. Given a base model, Neon first fine-tunes it on its own self-synthesized data but then, counterintuitively, reverses its gradient updates to extrapolate away from the degraded weights. We prove that Neon works because typical inference samplers that favor high-probability regions create a predictable anti-alignment between the synthetic and real data population gradients, which negative extrapolation corrects to better align the model with the true data distribution. Neon is remarkably easy to implement via a simple post-hoc merge that requires no new real data, works effectively with as few as 1k synthetic samples, and typically uses less than 1% additional training compute. We demonstrate Neon’s universality across a range of architectures (diffusion, flow matching, autoregressive, and inductive moment matching models) and datasets (ImageNet, CIFAR-10, and FFHQ). In particular, on ImageNet 256x256, Neon elevates the xAR-L model to a new state-of-the-art FID of 1.02 with only 0.36% additional training compute.
+
+---
+
+## Method
+
+![Algorithm 1: Neon — Negative Extrapolation from Self‑Training](https://github.com/SinaAlemohammad/Neon/raw/main/assets/algorithm.png)
+
+**In one line:** sample with your usual inference to form a synthetic set $S$; briefly fine-tune the reference model on $S$ to get $\theta_s$; then **reverse** that update with a merge $\theta_{\text{neon}}=(1+w)\,\theta_r - w\,\theta_s$ (small $w>0$), which cancels mode-seeking drift and improves FID.
+
+---
+
+## Benchmark Performance
+
+| Model type | Dataset | Base model FID | Neon FID (paper) | Download model |
+| :-------- | :--------------- | -------------: | ---------------: | :------------------------------------------------------------------------------------------------------- |
+| xAR-L | ImageNet-256 | 1.28 | **1.02** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_xARL_imagenet256.pth) |
+| xAR-B | ImageNet-256 | 1.72 | **1.31** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_xARB_imagenet256.pth) |
+| VAR d16 | ImageNet-256 | 3.30 | **2.01** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_VARd16_imagenet256.pth) |
+| VAR d36 | ImageNet-512 | 2.63 | **1.70** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_VARd36_imagenet512.pth) |
+| EDM (cond.) | CIFAR-10 (32×32) | 1.78 | **1.38** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_EDM_conditional_CIFAR10.pkl) |
+| EDM (uncond.) | CIFAR-10 (32×32) | 1.98 | **1.38** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_EDM_unconditional_CIFAR10.pkl) |
+| EDM | FFHQ-64×64 | 2.39 | **1.12** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_EDM_FFHQ.pkl) |
+| IMM | ImageNet-256 | 1.99 | **1.46** | [Download](https://huggingface.co/sinaalemohammad/Neon/resolve/main/Neon_imm_imagenet256.pkl) |
+
+---
+
+## 🚀 Quickstart
+
+### 1) Environment
+
+```bash
+# from repo root
+conda env create -f environment.yml
+conda activate neon
+```
+
+### 2) Download pretrained models & FID stats
+
+```bash
+bash download_models.sh
+```
+
+This populates `checkpoints/` and `fid_stats/`.
+**Pretrained Neon models can also be downloaded from Hugging Face:** [https://huggingface.co/sinaalemohammad/Neon](https://huggingface.co/sinaalemohammad/Neon)
+
+### 3) Evaluate (FID/IS)
+
+> All examples assume 8 GPUs; adjust `--nproc_per_node` / batch sizes as needed.
+
+**xAR @ ImageNet‑256**
+
+```bash
+# 1) VAE for xAR (credit: MAR)
+hf download xwen99/mar-vae-kl16 --include kl16.ckpt --local-dir xAR/pretrained
+# 2) Use it via:
+#   --vae_path xAR/pretrained/kl16.ckpt
+
+# xAR‑L
+PYTHONPATH=xAR torchrun --standalone --nproc_per_node=8 xAR/calculate_fid.py \
+  --model xar_large \
+  --model_ckpt checkpoints/Neon_xARL_imagenet256.pth \
+  --cfg 2.3 --vae_path xAR/pretrained/kl16.ckpt \
+  --num_images 50000 --batch_size 64 --flow_steps 40 --img_size 256 \
+  --fid_stats fid_stats/adm_in256_stats.npz
+
+# xAR‑B
+PYTHONPATH=xAR torchrun --standalone --nproc_per_node=8 xAR/calculate_fid.py \
+  --model xar_base \
+  --model_ckpt checkpoints/Neon_xARB_imagenet256.pth \
+  --cfg 2.7 --vae_path xAR/pretrained/kl16.ckpt \
+  --num_images 50000 --batch_size 32 --flow_steps 50 --img_size 256 \
+  --fid_stats fid_stats/adm_in256_stats.npz
+```
+
+**VAR @ ImageNet‑256 / 512**
+
+```bash
+# d16 @ 256
+PYTHONPATH=VAR/VAR_imagenet_256 torchrun --standalone --nproc_per_node=8 \
+  VAR/VAR_imagenet_256/calculate_fid.py \
+  --var_ckpt checkpoints/Neon_VARd16_imagenet256.pth \
+  --num_images 50000 --batch_size 64 --img_size 256 \
+  --fid_stats fid_stats/adm_in256_stats.npz
+
+# d36 @ 512
+PYTHONPATH=VAR/VAR_imagenet_512 torchrun --standalone --nproc_per_node=8 \
+  VAR/VAR_imagenet_512/calculate_fid.py \
+  --var_ckpt checkpoints/Neon_VARd36_imagenet512.pth \
+  --num_images 50000 --batch_size 32 --img_size 512 \
+  --fid_stats fid_stats/adm_in512_stats.npz
+```
+
+**EDM (Karras et al.) @ CIFAR‑10 / FFHQ**
+
+```bash
+# CIFAR‑10 (conditional)
+PYTHONPATH=edm torchrun --standalone --nproc_per_node=8 edm/calculate_fid.py \
+  --network_pkl checkpoints/Neon_EDM_conditional_CIFAR10.pkl \
+  --ref https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/cifar10-32x32.npz \
+  --seeds 0-49999 --max_batch_size 256 --num_steps 18
+
+# CIFAR‑10 (unconditional)
+PYTHONPATH=edm torchrun --standalone --nproc_per_node=8 edm/calculate_fid.py \
+  --network_pkl checkpoints/Neon_EDM_unconditional_CIFAR10.pkl \
+  --ref https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/cifar10-32x32.npz \
+  --seeds 0-49999 --max_batch_size 256 --num_steps 18
+
+# FFHQ‑64 (unconditional)
+PYTHONPATH=edm torchrun --standalone --nproc_per_node=8 edm/calculate_fid.py \
+  --network_pkl checkpoints/Neon_EDM_FFHQ.pkl \
+  --ref https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/ffhq-64x64.npz \
+  --seeds 0-49999 --max_batch_size 256 --num_steps 40
+```
+
+**IMM @ ImageNet‑256**
+
+```bash
+# IMM @ T = 8
+PYTHONPATH=imm torchrun --standalone --nproc_per_node=8 imm/calculate_fid.py \
+  --model_ckpt checkpoints/Neon_IMM_imagenet256.pth \
+  --num_images 50000 --batch_size 64 --img_size 256 \
+  --fid_stats fid_stats/adm_in256_stats.npz
+```
+
+---
+
+## 📣 Citation
+
+If you find Neon useful, please consider citing the paper:
+
+```bibtex
+@article{neon2025,
+  title={Neon: Negative Extrapolation from Self-Training for Generative Models},
+  author={Alemohammad, Sina and collaborators},
+  journal={arXiv preprint},
+  year={2025}
+}
+```